Anonymizing Classification Data for Privacy Preservation

By Benjamin C. M. Fung, Ke Wang, Philip S. Yu. IEEE Transactions on Knowledge and Data Engineering, 2007

430 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/247458842a593777bc64ca43347769aef3daa7db

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Citations in the corpus

k -Anonymous Data Mining: A Survey (2008, methodology): “InfoGain(v) then measures the reduction of entropy after refining v (for a formal definition of InfoGain(v) see [16]).”
Privacy-preserving data publishing: A survey of recent developments (2010, methodology): “Instead of bottom-up, the top-down specialization (TDS) method [Fung et al. 2005, 2007] generalizes a table by specializing it from the most general state in which all values are generalized to the most general values of their taxonomy trees.”
The application of differential privacy to health data (2012, methodology): “The authors performed a comparison between their algorithm and the top-down specialization approach of [27] that produces k -anonymous data.”
On static and dynamic methods for condensation-based privacy-preserving data mining (2008, methodology): “A number of papers have also appeared on the k -anonymity model [8; 7; 19; 17; 18; 23; 29; 38; 40] recently.”
Context-Aware Generative Adversarial Privacy (2017, background): “Other annoymization techniques, such as generalization [11, 32, 49] and suppression [41, 68, 86], also cannot prevent an adversary from performing the sensitive linkages or recover private information from published datasets [31].”
Secure Distributed Framework for Achieving ε-Differential Privacy (2012, background): “Blue-collar Any_Sex [60-99) ∪Cuti = {Any_Job, Any_Sex, [1-99)} Any_Job {White-collar, Blue-collar} ∪Cuti = {White-collar,Blue-collar, Any_Sex, [1,99)}”
Differentially private data release for data mining (2011, methodology): “Does the interactive approach offer better data mining results than the noninteractive approach? Does differentially private data provide less utility than k-anonymous data? Experimental results demonstrate that our algorithm outperforms the recently proposed differentially-private interactive algor”
Disease Diagnosis in Smart Healthcare: Innovation, Technologies and Applications (2017, methodology): “Common privacy-preserving methods include disclosure control [162,163], output perturbation [164,165] and anonymization [166,167].”
Privacy-Preserving Stochastic Gradual Learning (2018, background): “Based on these information, hackers can perform reverse engineering, namely reasoning from output to input, to re-identify data providers and expose their privacy [16,17].”
A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008, methodology): “Greedy approaches have been widely used in privacy preservation in relational data [34; 15; 10], and have been shown effective.”